A surface defect detection method, terminal equipment and storage medium

Abstract

The present invention relates to a surface defect detection method, terminal equipment and storage medium. The method includes: S1: collecting defect images of detection objects to form a training set, and marking the defect images in the training set; S2: constructing defect images The detection model is trained by inputting the training set into the defect detection model to obtain the trained defect detection model; the defect detection model is constructed based on the Faster R-CNN network, and its feature extraction network is a VGG-16 network, and the VGG-16 network , superimpose the output of the third layer and the output of the fifth layer; S3: Input the defect image to be detected into the trained defect detection model, and obtain the defect location frame and defect type in the defect image; S4: According to the defect image to be detected The defect localization frame in the defect image is used to segment the defect. The invention is based on the Faster R-CNN network and the threshold segmentation method, so that the defect type, position and outline can be output only by inputting an image during the detection process, that is, the end-to-end detection of defects is realized.

Description

technical field [0001] The present invention relates to the technical field of image detection, in particular to a surface defect detection method, a terminal device and a storage medium. Background technique [0002] Not only are there many types of steel surface defects, but the distribution is not fixed. A steel surface image to be inspected may have multiple types of defects; in addition, the problem of uneven illumination during image acquisition will interfere with the machine inspection process. Therefore, traditional production enterprises are still at the stage of manual steel surface inspection. In the production process of hot-rolled strip steel, because the production temperature is as high as hundreds of degrees Celsius, manual quality inspection can only be carried out at the end of the coil. The method of random inspection after production is not only unable to achieve effective control of the cause of defects, but also makes quality objections frequently occ...

AI Technical Summary

Problems solved by technology

[0004] 1) The processing process is complex, and the defect determination process requires manual design of features, which raises the threshold for monitoring system development;

[0005] 2) The generalization ability of manually designed defect features is poor, and it is not suitable for new detection objects and defect types;

[0006] 3) Limited to defect location and classification, it is temporarily impossible to segment defect contours, which makes it impossible to achieve effective negative feedback in the manufacturing process to adjust production parameters

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